Health monitoring of rotating ma-chines
Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc.
This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults.
However, few of them consider different failure types using heterogeneous data and various operating conditions. For this reason, to help researchers to develop new algorithms, multiple data caried from different rotating machines presenting multiple critical components in various operating conditions and sensor data are presented.